Overview

Dataset statistics

Number of variables24
Number of observations991346
Missing cells0
Missing cells (%)0.0%
Duplicate rows26
Duplicate rows (%)< 0.1%
Total size in memory181.5 MiB
Average record size in memory192.0 B

Variable types

Categorical4
Numeric19
Boolean1

Alerts

Dataset has 26 (< 0.1%) duplicate rowsDuplicates
height is highly overall correlated with weight and 2 other fieldsHigh correlation
weight is highly overall correlated with height and 3 other fieldsHigh correlation
waistline is highly overall correlated with weightHigh correlation
sight_left is highly overall correlated with sight_rightHigh correlation
sight_right is highly overall correlated with sight_leftHigh correlation
SBP is highly overall correlated with DBPHigh correlation
DBP is highly overall correlated with SBPHigh correlation
tot_chole is highly overall correlated with LDL_choleHigh correlation
LDL_chole is highly overall correlated with tot_choleHigh correlation
hemoglobin is highly overall correlated with height and 2 other fieldsHigh correlation
SGOT_AST is highly overall correlated with SGOT_ALTHigh correlation
SGOT_ALT is highly overall correlated with SGOT_AST and 1 other fieldsHigh correlation
gamma_GTP is highly overall correlated with SGOT_ALTHigh correlation
sex is highly overall correlated with height and 3 other fieldsHigh correlation
hear_left is highly overall correlated with hear_rightHigh correlation
hear_right is highly overall correlated with hear_leftHigh correlation
SMK_stat_type_cd is highly overall correlated with sexHigh correlation
hear_left is highly imbalanced (79.8%)Imbalance
hear_right is highly imbalanced (80.3%)Imbalance
waistline is highly skewed (γ1 = 26.78843978)Skewed
HDL_chole is highly skewed (γ1 = 104.5776351)Skewed
serum_creatinine is highly skewed (γ1 = 111.022058)Skewed
SGOT_AST is highly skewed (γ1 = 150.4916897)Skewed
SGOT_ALT is highly skewed (γ1 = 50.03887229)Skewed

Reproduction

Analysis started2023-10-15 14:42:11.203264
Analysis finished2023-10-15 14:46:10.680089
Duration3 minutes and 59.48 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

sex
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Male
526415 
Female
464931 

Length

Max length6
Median length4
Mean length4.9379793
Min length4

Characters and Unicode

Total characters4895246
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 526415
53.1%
Female 464931
46.9%

Length

2023-10-15T14:46:10.864218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-15T14:46:11.149794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
male 526415
53.1%
female 464931
46.9%

Most occurring characters

ValueCountFrequency (%)
e 1456277
29.7%
a 991346
20.3%
l 991346
20.3%
M 526415
 
10.8%
F 464931
 
9.5%
m 464931
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3903900
79.7%
Uppercase Letter 991346
 
20.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1456277
37.3%
a 991346
25.4%
l 991346
25.4%
m 464931
 
11.9%
Uppercase Letter
ValueCountFrequency (%)
M 526415
53.1%
F 464931
46.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 4895246
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1456277
29.7%
a 991346
20.3%
l 991346
20.3%
M 526415
 
10.8%
F 464931
 
9.5%
m 464931
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4895246
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1456277
29.7%
a 991346
20.3%
l 991346
20.3%
M 526415
 
10.8%
F 464931
 
9.5%
m 464931
 
9.5%

age
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.614491
Minimum20
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-10-15T14:46:11.337393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile25
Q135
median45
Q360
95-th percentile70
Maximum85
Range65
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.181339
Coefficient of variation (CV)0.29783662
Kurtosis-0.57561552
Mean47.614491
Median Absolute Deviation (MAD)10
Skewness0.15365339
Sum47202435
Variance201.11038
MonotonicityNot monotonic
2023-10-15T14:46:11.597771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
40 130385
13.2%
50 129434
13.1%
45 118355
11.9%
55 111223
11.2%
60 106063
10.7%
35 84726
8.5%
30 77600
7.8%
25 64370
6.5%
65 52961
5.3%
70 50666
 
5.1%
Other values (4) 65563
6.6%
ValueCountFrequency (%)
20 21971
 
2.2%
25 64370
6.5%
30 77600
7.8%
35 84726
8.5%
40 130385
13.2%
45 118355
11.9%
50 129434
13.1%
55 111223
11.2%
60 106063
10.7%
65 52961
5.3%
ValueCountFrequency (%)
85 3291
 
0.3%
80 14968
 
1.5%
75 25333
 
2.6%
70 50666
 
5.1%
65 52961
5.3%
60 106063
10.7%
55 111223
11.2%
50 129434
13.1%
45 118355
11.9%
40 130385
13.2%

height
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.24063
Minimum130
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-10-15T14:46:11.842613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile145
Q1155
median160
Q3170
95-th percentile175
Maximum190
Range60
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.2829575
Coefficient of variation (CV)0.057217219
Kurtosis-0.53564034
Mean162.24063
Median Absolute Deviation (MAD)5
Skewness-0.02273717
Sum1.608366 × 108
Variance86.173299
MonotonicityNot monotonic
2023-10-15T14:46:12.054497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
160 181809
18.3%
165 178228
18.0%
170 166328
16.8%
155 165678
16.7%
150 107929
10.9%
175 98850
10.0%
145 39176
 
4.0%
180 35970
 
3.6%
140 9100
 
0.9%
185 6588
 
0.7%
Other values (3) 1690
 
0.2%
ValueCountFrequency (%)
130 86
 
< 0.1%
135 1241
 
0.1%
140 9100
 
0.9%
145 39176
 
4.0%
150 107929
10.9%
155 165678
16.7%
160 181809
18.3%
165 178228
18.0%
170 166328
16.8%
175 98850
10.0%
ValueCountFrequency (%)
190 363
 
< 0.1%
185 6588
 
0.7%
180 35970
 
3.6%
175 98850
10.0%
170 166328
16.8%
165 178228
18.0%
160 181809
18.3%
155 165678
16.7%
150 107929
10.9%
145 39176
 
4.0%

weight
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.28405
Minimum25
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-10-15T14:46:12.303522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile45
Q155
median60
Q370
95-th percentile85
Maximum140
Range115
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.514241
Coefficient of variation (CV)0.19774715
Kurtosis0.35922025
Mean63.28405
Median Absolute Deviation (MAD)10
Skewness0.5765566
Sum62736390
Variance156.60622
MonotonicityNot monotonic
2023-10-15T14:46:12.591221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
60 151134
15.2%
55 150415
15.2%
65 141241
14.2%
50 125079
12.6%
70 122281
12.3%
75 90207
9.1%
45 63047
6.4%
80 58176
 
5.9%
85 33708
 
3.4%
90 18250
 
1.8%
Other values (14) 37808
 
3.8%
ValueCountFrequency (%)
25 9
 
< 0.1%
30 157
 
< 0.1%
35 1948
 
0.2%
40 16639
 
1.7%
45 63047
6.4%
50 125079
12.6%
55 150415
15.2%
60 151134
15.2%
65 141241
14.2%
70 122281
12.3%
ValueCountFrequency (%)
140 3
 
< 0.1%
135 5
 
< 0.1%
130 43
 
< 0.1%
125 80
 
< 0.1%
120 236
 
< 0.1%
115 573
 
0.1%
110 1177
 
0.1%
105 2454
 
0.2%
100 4829
0.5%
95 9655
1.0%

waistline
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct737
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.233358
Minimum8
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-10-15T14:46:12.880073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile66
Q174.1
median81
Q387.8
95-th percentile97
Maximum999
Range991
Interquartile range (IQR)13.7

Descriptive statistics

Standard deviation11.850323
Coefficient of variation (CV)0.14588001
Kurtosis2066.8122
Mean81.233358
Median Absolute Deviation (MAD)6.8
Skewness26.78844
Sum80530364
Variance140.43016
MonotonicityNot monotonic
2023-10-15T14:46:13.182112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 37790
 
3.8%
81 34603
 
3.5%
82 34024
 
3.4%
84 33913
 
3.4%
86 32723
 
3.3%
83 32282
 
3.3%
76 31254
 
3.2%
78 30832
 
3.1%
85 30626
 
3.1%
79 28853
 
2.9%
Other values (727) 664446
67.0%
ValueCountFrequency (%)
8 1
 
< 0.1%
27 1
 
< 0.1%
30 2
< 0.1%
32 3
< 0.1%
35 2
< 0.1%
40 1
 
< 0.1%
42 1
 
< 0.1%
43 1
 
< 0.1%
48 1
 
< 0.1%
49 1
 
< 0.1%
ValueCountFrequency (%)
999 57
< 0.1%
149.1 1
 
< 0.1%
145 1
 
< 0.1%
140 1
 
< 0.1%
138 1
 
< 0.1%
136.8 1
 
< 0.1%
136 2
 
< 0.1%
135 1
 
< 0.1%
134 3
 
< 0.1%
133 1
 
< 0.1%

sight_left
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98083434
Minimum0.1
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-10-15T14:46:13.451865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.7
median1
Q31.2
95-th percentile1.5
Maximum9.9
Range9.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.60594863
Coefficient of variation (CV)0.61778897
Kurtosis144.94968
Mean0.98083434
Median Absolute Deviation (MAD)0.2
Skewness9.994626
Sum972346.2
Variance0.36717375
MonotonicityNot monotonic
2023-10-15T14:46:13.718584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 201418
20.3%
1.2 188460
19.0%
1.5 121713
12.3%
0.9 105297
10.6%
0.8 99913
10.1%
0.7 83749
8.4%
0.6 53644
 
5.4%
0.5 51895
 
5.2%
0.4 30744
 
3.1%
0.3 20388
 
2.1%
Other values (14) 34125
 
3.4%
ValueCountFrequency (%)
0.1 9503
 
1.0%
0.2 12255
 
1.2%
0.3 20388
 
2.1%
0.4 30744
 
3.1%
0.5 51895
 
5.2%
0.6 53644
 
5.4%
0.7 83749
8.4%
0.8 99913
10.1%
0.9 105297
10.6%
1 201418
20.3%
ValueCountFrequency (%)
9.9 3118
 
0.3%
2.5 7
 
< 0.1%
2.2 2
 
< 0.1%
2.1 3
 
< 0.1%
2 8452
 
0.9%
1.9 32
 
< 0.1%
1.8 25
 
< 0.1%
1.7 14
 
< 0.1%
1.6 371
 
< 0.1%
1.5 121713
12.3%

sight_right
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97842913
Minimum0.1
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-10-15T14:46:13.970674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.7
median1
Q31.2
95-th percentile1.5
Maximum9.9
Range9.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.60477411
Coefficient of variation (CV)0.61810722
Kurtosis145.92255
Mean0.97842913
Median Absolute Deviation (MAD)0.2
Skewness10.033647
Sum969961.8
Variance0.36575173
MonotonicityNot monotonic
2023-10-15T14:46:14.237997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 204493
20.6%
1.2 187266
18.9%
1.5 120620
12.2%
0.9 106186
10.7%
0.8 98777
10.0%
0.7 84168
8.5%
0.6 53238
 
5.4%
0.5 50803
 
5.1%
0.4 31318
 
3.2%
0.3 20090
 
2.0%
Other values (14) 34387
 
3.5%
ValueCountFrequency (%)
0.1 10028
 
1.0%
0.2 13002
 
1.3%
0.3 20090
 
2.0%
0.4 31318
 
3.2%
0.5 50803
 
5.1%
0.6 53238
 
5.4%
0.7 84168
8.5%
0.8 98777
10.0%
0.9 106186
10.7%
1 204493
20.6%
ValueCountFrequency (%)
9.9 3111
 
0.3%
2.5 10
 
< 0.1%
2.2 1
 
< 0.1%
2.1 10
 
< 0.1%
2 7363
 
0.7%
1.9 21
 
< 0.1%
1.8 32
 
< 0.1%
1.7 24
 
< 0.1%
1.6 390
 
< 0.1%
1.5 120620
12.2%

hear_left
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
1.0
960124 
2.0
 
31222

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2974038
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 960124
96.9%
2.0 31222
 
3.1%

Length

2023-10-15T14:46:14.491933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-15T14:46:14.738549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 960124
96.9%
2.0 31222
 
3.1%

Most occurring characters

ValueCountFrequency (%)
. 991346
33.3%
0 991346
33.3%
1 960124
32.3%
2 31222
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1982692
66.7%
Other Punctuation 991346
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 991346
50.0%
1 960124
48.4%
2 31222
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 991346
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2974038
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 991346
33.3%
0 991346
33.3%
1 960124
32.3%
2 31222
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2974038
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 991346
33.3%
0 991346
33.3%
1 960124
32.3%
2 31222
 
1.0%

hear_right
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
1.0
961134 
2.0
 
30212

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2974038
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 961134
97.0%
2.0 30212
 
3.0%

Length

2023-10-15T14:46:14.940420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-15T14:46:15.172606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 961134
97.0%
2.0 30212
 
3.0%

Most occurring characters

ValueCountFrequency (%)
. 991346
33.3%
0 991346
33.3%
1 961134
32.3%
2 30212
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1982692
66.7%
Other Punctuation 991346
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 991346
50.0%
1 961134
48.5%
2 30212
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 991346
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2974038
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 991346
33.3%
0 991346
33.3%
1 961134
32.3%
2 30212
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2974038
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 991346
33.3%
0 991346
33.3%
1 961134
32.3%
2 30212
 
1.0%

SBP
Real number (ℝ)

HIGH CORRELATION 

Distinct171
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.4325
Minimum67
Maximum273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-10-15T14:46:15.405777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile100
Q1112
median120
Q3131
95-th percentile148
Maximum273
Range206
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.543148
Coefficient of variation (CV)0.11878503
Kurtosis0.99663922
Mean122.4325
Median Absolute Deviation (MAD)10
Skewness0.48206032
Sum1.2137297 × 108
Variance211.50315
MonotonicityNot monotonic
2023-10-15T14:46:15.720859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 78786
 
7.9%
110 72193
 
7.3%
130 71714
 
7.2%
118 40078
 
4.0%
100 30829
 
3.1%
138 24426
 
2.5%
119 24166
 
2.4%
128 23766
 
2.4%
124 22224
 
2.2%
116 22177
 
2.2%
Other values (161) 580987
58.6%
ValueCountFrequency (%)
67 1
 
< 0.1%
70 3
 
< 0.1%
72 1
 
< 0.1%
73 4
 
< 0.1%
74 3
 
< 0.1%
75 8
< 0.1%
76 7
< 0.1%
77 6
< 0.1%
78 11
< 0.1%
79 6
< 0.1%
ValueCountFrequency (%)
273 1
< 0.1%
270 1
< 0.1%
255 1
< 0.1%
253 1
< 0.1%
244 1
< 0.1%
241 1
< 0.1%
240 1
< 0.1%
238 1
< 0.1%
236 1
< 0.1%
235 1
< 0.1%

DBP
Real number (ℝ)

HIGH CORRELATION 

Distinct127
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.052627
Minimum32
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-10-15T14:46:16.006523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile60
Q170
median76
Q382
95-th percentile92
Maximum185
Range153
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.8893654
Coefficient of variation (CV)0.13003318
Kurtosis0.89150383
Mean76.052627
Median Absolute Deviation (MAD)6
Skewness0.4000338
Sum75394468
Variance97.799547
MonotonicityNot monotonic
2023-10-15T14:46:16.293750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 123156
 
12.4%
70 111699
 
11.3%
78 44628
 
4.5%
60 41253
 
4.2%
72 33644
 
3.4%
75 32575
 
3.3%
76 31976
 
3.2%
74 31773
 
3.2%
82 27195
 
2.7%
90 25959
 
2.6%
Other values (117) 487488
49.2%
ValueCountFrequency (%)
32 1
 
< 0.1%
33 1
 
< 0.1%
34 1
 
< 0.1%
36 2
 
< 0.1%
37 3
 
< 0.1%
38 1
 
< 0.1%
39 3
 
< 0.1%
40 14
< 0.1%
41 7
< 0.1%
42 12
< 0.1%
ValueCountFrequency (%)
185 1
< 0.1%
181 1
< 0.1%
180 1
< 0.1%
170 1
< 0.1%
164 1
< 0.1%
163 1
< 0.1%
160 2
< 0.1%
156 2
< 0.1%
154 2
< 0.1%
153 2
< 0.1%

BLDS
Real number (ℝ)

Distinct498
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.42445
Minimum25
Maximum852
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-10-15T14:46:17.332108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile79
Q188
median96
Q3105
95-th percentile137
Maximum852
Range827
Interquartile range (IQR)17

Descriptive statistics

Standard deviation24.17996
Coefficient of variation (CV)0.24077762
Kurtosis40.470487
Mean100.42445
Median Absolute Deviation (MAD)8
Skewness4.6173775
Sum99555374
Variance584.67045
MonotonicityNot monotonic
2023-10-15T14:46:17.610857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93 35243
 
3.6%
92 35227
 
3.6%
95 35190
 
3.5%
94 35173
 
3.5%
91 34389
 
3.5%
96 33814
 
3.4%
90 33754
 
3.4%
97 32981
 
3.3%
89 32178
 
3.2%
98 31902
 
3.2%
Other values (488) 651495
65.7%
ValueCountFrequency (%)
25 1
 
< 0.1%
30 1
 
< 0.1%
32 1
 
< 0.1%
33 2
< 0.1%
34 2
< 0.1%
36 2
< 0.1%
37 1
 
< 0.1%
38 4
< 0.1%
39 1
 
< 0.1%
40 1
 
< 0.1%
ValueCountFrequency (%)
852 1
< 0.1%
801 1
< 0.1%
800 1
< 0.1%
784 1
< 0.1%
769 1
< 0.1%
741 1
< 0.1%
685 1
< 0.1%
663 1
< 0.1%
638 1
< 0.1%
629 2
< 0.1%

tot_chole
Real number (ℝ)

HIGH CORRELATION 

Distinct474
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195.55702
Minimum30
Maximum2344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-10-15T14:46:17.894023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile137
Q1169
median193
Q3219
95-th percentile261
Maximum2344
Range2314
Interquartile range (IQR)50

Descriptive statistics

Standard deviation38.660155
Coefficient of variation (CV)0.19769249
Kurtosis49.462386
Mean195.55702
Median Absolute Deviation (MAD)25
Skewness1.5568817
Sum1.9386467 × 108
Variance1494.6076
MonotonicityNot monotonic
2023-10-15T14:46:18.169550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199 11079
 
1.1%
184 10873
 
1.1%
189 10857
 
1.1%
190 10825
 
1.1%
188 10796
 
1.1%
197 10775
 
1.1%
192 10746
 
1.1%
187 10746
 
1.1%
196 10723
 
1.1%
186 10717
 
1.1%
Other values (464) 883209
89.1%
ValueCountFrequency (%)
30 1
 
< 0.1%
45 1
 
< 0.1%
54 1
 
< 0.1%
55 1
 
< 0.1%
57 3
< 0.1%
58 1
 
< 0.1%
59 1
 
< 0.1%
60 1
 
< 0.1%
62 1
 
< 0.1%
63 2
< 0.1%
ValueCountFrequency (%)
2344 1
< 0.1%
2196 1
< 0.1%
2067 1
< 0.1%
2046 1
< 0.1%
2033 1
< 0.1%
1815 1
< 0.1%
1736 1
< 0.1%
1619 1
< 0.1%
1605 1
< 0.1%
1575 1
< 0.1%

HDL_chole
Real number (ℝ)

SKEWED 

Distinct223
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.9368
Minimum1
Maximum8110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-10-15T14:46:18.454669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile36
Q146
median55
Q366
95-th percentile84
Maximum8110
Range8109
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.238479
Coefficient of variation (CV)0.30276515
Kurtosis48094.155
Mean56.9368
Median Absolute Deviation (MAD)10
Skewness104.57764
Sum56444069
Variance297.16516
MonotonicityNot monotonic
2023-10-15T14:46:18.783130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 29602
 
3.0%
52 28335
 
2.9%
53 28323
 
2.9%
51 28126
 
2.8%
54 27952
 
2.8%
49 27869
 
2.8%
48 27428
 
2.8%
55 27092
 
2.7%
56 26827
 
2.7%
47 26476
 
2.7%
Other values (213) 713316
72.0%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 7
< 0.1%
3 3
 
< 0.1%
4 5
 
< 0.1%
5 2
 
< 0.1%
6 6
 
< 0.1%
7 12
< 0.1%
8 6
 
< 0.1%
9 11
< 0.1%
10 15
< 0.1%
ValueCountFrequency (%)
8110 1
< 0.1%
1206 1
< 0.1%
933 1
< 0.1%
797 1
< 0.1%
727 1
< 0.1%
701 1
< 0.1%
697 1
< 0.1%
677 1
< 0.1%
658 1
< 0.1%
636 1
< 0.1%

LDL_chole
Real number (ℝ)

HIGH CORRELATION 

Distinct432
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.03769
Minimum1
Maximum5119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-10-15T14:46:19.118904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile60
Q189
median111
Q3135
95-th percentile172
Maximum5119
Range5118
Interquartile range (IQR)46

Descriptive statistics

Standard deviation35.842812
Coefficient of variation (CV)0.31708726
Kurtosis481.28298
Mean113.03769
Median Absolute Deviation (MAD)23
Skewness5.2517394
Sum1.1205946 × 108
Variance1284.7072
MonotonicityNot monotonic
2023-10-15T14:46:19.586103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109 11824
 
1.2%
104 11795
 
1.2%
107 11782
 
1.2%
110 11773
 
1.2%
102 11740
 
1.2%
112 11656
 
1.2%
115 11631
 
1.2%
108 11611
 
1.2%
105 11607
 
1.2%
106 11597
 
1.2%
Other values (422) 874330
88.2%
ValueCountFrequency (%)
1 81
< 0.1%
2 13
 
< 0.1%
3 13
 
< 0.1%
4 11
 
< 0.1%
5 20
 
< 0.1%
6 23
 
< 0.1%
7 29
 
< 0.1%
8 40
< 0.1%
9 31
 
< 0.1%
10 39
< 0.1%
ValueCountFrequency (%)
5119 1
< 0.1%
2254 1
< 0.1%
2114 1
< 0.1%
2111 1
< 0.1%
2043 1
< 0.1%
2026 1
< 0.1%
1933 1
< 0.1%
1798 1
< 0.1%
1750 1
< 0.1%
1696 1
< 0.1%

triglyceride
Real number (ℝ)

Distinct1657
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.14175
Minimum1
Maximum9490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-10-15T14:46:20.037711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile46
Q173
median106
Q3159
95-th percentile297
Maximum9490
Range9489
Interquartile range (IQR)86

Descriptive statistics

Standard deviation102.19698
Coefficient of variation (CV)0.77338906
Kurtosis175.38524
Mean132.14175
Median Absolute Deviation (MAD)39
Skewness6.5293729
Sum1.309982 × 108
Variance10444.224
MonotonicityNot monotonic
2023-10-15T14:46:20.572720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72 8236
 
0.8%
78 8207
 
0.8%
79 8178
 
0.8%
69 8139
 
0.8%
70 8131
 
0.8%
76 8122
 
0.8%
68 8120
 
0.8%
82 8102
 
0.8%
75 8096
 
0.8%
77 8095
 
0.8%
Other values (1647) 909920
91.8%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 2
 
< 0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
7 10
< 0.1%
8 7
< 0.1%
9 11
< 0.1%
10 8
< 0.1%
ValueCountFrequency (%)
9490 1
< 0.1%
6430 1
< 0.1%
6173 1
< 0.1%
5236 1
< 0.1%
4164 1
< 0.1%
4000 1
< 0.1%
3858 1
< 0.1%
3848 1
< 0.1%
3830 1
< 0.1%
3771 1
< 0.1%

hemoglobin
Real number (ℝ)

HIGH CORRELATION 

Distinct190
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.229824
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-10-15T14:46:21.083422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.7
Q113.2
median14.3
Q315.4
95-th percentile16.6
Maximum25
Range24
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.5849287
Coefficient of variation (CV)0.11138077
Kurtosis0.71137942
Mean14.229824
Median Absolute Deviation (MAD)1.1
Skewness-0.3839878
Sum14106679
Variance2.5119991
MonotonicityNot monotonic
2023-10-15T14:46:21.587826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.5 23297
 
2.4%
14 23108
 
2.3%
13.6 23093
 
2.3%
13.4 22946
 
2.3%
13.8 22781
 
2.3%
13.3 22734
 
2.3%
13.9 22635
 
2.3%
15 22600
 
2.3%
13.7 22591
 
2.3%
14.8 22181
 
2.2%
Other values (180) 763380
77.0%
ValueCountFrequency (%)
1 3
< 0.1%
2.8 1
 
< 0.1%
3.7 3
< 0.1%
3.8 1
 
< 0.1%
3.9 3
< 0.1%
4 4
< 0.1%
4.1 2
< 0.1%
4.2 4
< 0.1%
4.3 3
< 0.1%
4.4 2
< 0.1%
ValueCountFrequency (%)
25 2
< 0.1%
24.2 1
< 0.1%
23.9 1
< 0.1%
23.6 1
< 0.1%
23.3 1
< 0.1%
22.7 1
< 0.1%
22.1 1
< 0.1%
22 1
< 0.1%
21.8 1
< 0.1%
21.7 2
< 0.1%

urine_protein
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0942244
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-10-15T14:46:22.022170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.43772355
Coefficient of variation (CV)0.40003087
Kurtosis36.899552
Mean1.0942244
Median Absolute Deviation (MAD)0
Skewness5.6724908
Sum1084755
Variance0.19160191
MonotonicityNot monotonic
2023-10-15T14:46:22.459087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 935175
94.3%
2 30850
 
3.1%
3 16405
 
1.7%
4 6427
 
0.6%
5 1977
 
0.2%
6 512
 
0.1%
ValueCountFrequency (%)
1 935175
94.3%
2 30850
 
3.1%
3 16405
 
1.7%
4 6427
 
0.6%
5 1977
 
0.2%
6 512
 
0.1%
ValueCountFrequency (%)
6 512
 
0.1%
5 1977
 
0.2%
4 6427
 
0.6%
3 16405
 
1.7%
2 30850
 
3.1%
1 935175
94.3%

serum_creatinine
Real number (ℝ)

SKEWED 

Distinct183
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86046668
Minimum0.1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-10-15T14:46:22.952611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q10.7
median0.8
Q31
95-th percentile1.2
Maximum98
Range97.9
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.48053042
Coefficient of variation (CV)0.55845326
Kurtosis19089.83
Mean0.86046668
Median Absolute Deviation (MAD)0.1
Skewness111.02206
Sum853020.2
Variance0.23090948
MonotonicityNot monotonic
2023-10-15T14:46:23.497463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 194902
19.7%
0.9 180626
18.2%
0.7 164293
16.6%
1 140743
14.2%
0.6 109236
11.0%
1.1 86355
8.7%
1.2 40744
 
4.1%
0.5 38932
 
3.9%
1.3 15160
 
1.5%
0.4 6050
 
0.6%
Other values (173) 14305
 
1.4%
ValueCountFrequency (%)
0.1 425
 
< 0.1%
0.2 99
 
< 0.1%
0.3 597
 
0.1%
0.4 6050
 
0.6%
0.5 38932
 
3.9%
0.6 109236
11.0%
0.7 164293
16.6%
0.8 194902
19.7%
0.9 180626
18.2%
1 140743
14.2%
ValueCountFrequency (%)
98 2
< 0.1%
96 2
< 0.1%
95 1
< 0.1%
94 1
< 0.1%
93 1
< 0.1%
87 1
< 0.1%
85 1
< 0.1%
81 1
< 0.1%
80 1
< 0.1%
79 1
< 0.1%

SGOT_AST
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct568
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.989308
Minimum1
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-10-15T14:46:24.057086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q119
median23
Q328
95-th percentile46
Maximum9999
Range9998
Interquartile range (IQR)9

Descriptive statistics

Standard deviation23.493386
Coefficient of variation (CV)0.90396349
Kurtosis50432.651
Mean25.989308
Median Absolute Deviation (MAD)5
Skewness150.49169
Sum25764397
Variance551.93919
MonotonicityNot monotonic
2023-10-15T14:46:24.475936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 64900
 
6.5%
21 63845
 
6.4%
19 62846
 
6.3%
22 61195
 
6.2%
18 58423
 
5.9%
23 57642
 
5.8%
24 52476
 
5.3%
17 50161
 
5.1%
25 47304
 
4.8%
26 41665
 
4.2%
Other values (558) 430889
43.5%
ValueCountFrequency (%)
1 14
 
< 0.1%
2 19
 
< 0.1%
3 16
 
< 0.1%
4 28
 
< 0.1%
5 39
 
< 0.1%
6 70
 
< 0.1%
7 131
 
< 0.1%
8 297
 
< 0.1%
9 580
 
0.1%
10 1708
0.2%
ValueCountFrequency (%)
9999 1
< 0.1%
7000 2
< 0.1%
3742 1
< 0.1%
3440 1
< 0.1%
3235 1
< 0.1%
2670 1
< 0.1%
1962 1
< 0.1%
1911 1
< 0.1%
1870 1
< 0.1%
1686 1
< 0.1%

SGOT_ALT
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct594
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.755051
Minimum1
Maximum7210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-10-15T14:46:24.765883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q115
median20
Q329
95-th percentile59
Maximum7210
Range7209
Interquartile range (IQR)14

Descriptive statistics

Standard deviation26.308599
Coefficient of variation (CV)1.0214928
Kurtosis8615.9443
Mean25.755051
Median Absolute Deviation (MAD)7
Skewness50.038872
Sum25532167
Variance692.1424
MonotonicityNot monotonic
2023-10-15T14:46:25.087963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 47841
 
4.8%
16 47260
 
4.8%
14 47175
 
4.8%
17 46075
 
4.6%
18 44336
 
4.5%
13 44024
 
4.4%
19 41894
 
4.2%
12 40938
 
4.1%
20 38803
 
3.9%
21 36256
 
3.7%
Other values (584) 556744
56.2%
ValueCountFrequency (%)
1 31
 
< 0.1%
2 60
 
< 0.1%
3 186
 
< 0.1%
4 567
 
0.1%
5 1636
 
0.2%
6 3259
 
0.3%
7 6591
 
0.7%
8 12212
 
1.2%
9 18937
1.9%
10 31707
3.2%
ValueCountFrequency (%)
7210 1
< 0.1%
4633 1
< 0.1%
3807 1
< 0.1%
3517 1
< 0.1%
3307 1
< 0.1%
2981 1
< 0.1%
2698 1
< 0.1%
2535 1
< 0.1%
2530 1
< 0.1%
2309 1
< 0.1%

gamma_GTP
Real number (ℝ)

HIGH CORRELATION 

Distinct940
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.136347
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2023-10-15T14:46:25.390137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q116
median23
Q339
95-th percentile105
Maximum999
Range998
Interquartile range (IQR)23

Descriptive statistics

Standard deviation50.424153
Coefficient of variation (CV)1.3578113
Kurtosis97.042135
Mean37.136347
Median Absolute Deviation (MAD)9
Skewness7.7185093
Sum36814969
Variance2542.5952
MonotonicityNot monotonic
2023-10-15T14:46:25.683526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 41158
 
4.2%
15 40932
 
4.1%
13 39919
 
4.0%
16 39750
 
4.0%
17 37763
 
3.8%
12 36681
 
3.7%
18 35756
 
3.6%
19 33419
 
3.4%
11 31565
 
3.2%
20 31362
 
3.2%
Other values (930) 623041
62.8%
ValueCountFrequency (%)
1 16
 
< 0.1%
2 31
 
< 0.1%
3 206
 
< 0.1%
4 239
 
< 0.1%
5 621
 
0.1%
6 1623
 
0.2%
7 3493
 
0.4%
8 8202
 
0.8%
9 14181
1.4%
10 25643
2.6%
ValueCountFrequency (%)
999 239
< 0.1%
998 1
 
< 0.1%
997 1
 
< 0.1%
996 2
 
< 0.1%
994 2
 
< 0.1%
993 4
 
< 0.1%
992 2
 
< 0.1%
991 1
 
< 0.1%
990 5
 
< 0.1%
989 2
 
< 0.1%

SMK_stat_type_cd
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
1.0
602441 
3.0
213954 
2.0
174951 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2974038
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row3.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 602441
60.8%
3.0 213954
 
21.6%
2.0 174951
 
17.6%

Length

2023-10-15T14:46:25.955852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-15T14:46:26.224211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 602441
60.8%
3.0 213954
 
21.6%
2.0 174951
 
17.6%

Most occurring characters

ValueCountFrequency (%)
. 991346
33.3%
0 991346
33.3%
1 602441
20.3%
3 213954
 
7.2%
2 174951
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1982692
66.7%
Other Punctuation 991346
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 991346
50.0%
1 602441
30.4%
3 213954
 
10.8%
2 174951
 
8.8%
Other Punctuation
ValueCountFrequency (%)
. 991346
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2974038
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 991346
33.3%
0 991346
33.3%
1 602441
20.3%
3 213954
 
7.2%
2 174951
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2974038
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 991346
33.3%
0 991346
33.3%
1 602441
20.3%
3 213954
 
7.2%
2 174951
 
5.9%

DRK_YN
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size968.2 KiB
False
495858 
True
495488 
ValueCountFrequency (%)
False 495858
50.0%
True 495488
50.0%
2023-10-15T14:46:26.437516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Interactions

2023-10-15T14:45:54.609396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:28.752851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:37.526459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:44.746305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:53.623719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:00.422318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:09.059381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:16.233525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:25.257758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:36.779389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:43.910851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:52.158211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:59.455854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:08.376489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:15.120770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:23.808640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:30.599837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:39.340964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:46.062916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:54.995220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:29.147167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:37.900977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:45.119309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:53.977131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:00.797526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:09.428160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:16.601073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:25.861074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:37.298171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:44.281686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:52.750828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:59.823060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:08.726742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:15.514780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:24.164141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:30.954535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:39.698211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:46.430094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:55.369741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:29.522594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:38.275235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:45.482770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:54.342986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:01.156134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:09.810960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:17.092085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:26.397328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:37.747155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:44.652340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:53.159449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:00.186100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:09.089382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:15.892421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:24.512806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:31.319450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:40.063625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:46.812494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:55.742320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:29.907305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:38.646935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:45.853447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:54.681313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:01.652433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:10.176626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:17.632908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:26.951684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:38.131010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:45.022827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:53.521829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:00.538559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:09.455614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:16.298404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:24.881680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:31.681777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:40.438184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:47.188671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:56.122301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:30.295842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:39.211425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:46.340951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:55.037279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:02.138159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:10.848579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:18.158728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:27.468869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:38.471710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:45.399519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:53.894095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:00.896166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:09.808508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:16.688849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:25.232813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:32.043574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:40.778702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:47.558206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:56.480551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:30.659173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:39.575825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:46.885474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:55.405005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:02.706983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:11.203121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:18.624813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:27.994295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:38.829702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:45.754023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:54.252542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:01.246936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:10.165963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:17.068126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:25.590900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:32.392278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:41.120984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:47.934074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:56.847389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:31.141237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:39.933342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:47.370857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:55.771678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:03.236402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:11.559098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:19.067852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:28.962989image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:39.207200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:46.105229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:54.608452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:01.611436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:10.512587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:17.430993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:25.968347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:32.739342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:41.476007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:48.286461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:57.219129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:31.676192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:40.298752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:47.905792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:56.105194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:03.759729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:11.906630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:19.532743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:29.957581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:39.540998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:46.430981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:44:54.959495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:01.961704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:10.849692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:17.795791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:26.309720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:33.085747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:41.801381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:48.648468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:57.594149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:32.240656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:40.711396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:48.490868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:43:56.469978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-10-15T14:45:23.373981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:30.258348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:38.990582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:45.719301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-15T14:45:54.240339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-15T14:46:26.648915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ageheightweightwaistlinesight_leftsight_rightSBPDBPBLDStot_choleHDL_choleLDL_choletriglyceridehemoglobinurine_proteinserum_creatinineSGOT_ASTSGOT_ALTgamma_GTPsexhear_lefthear_rightSMK_stat_type_cdDRK_YN
age1.000-0.385-0.1730.165-0.387-0.3800.2620.1190.2540.024-0.1140.0390.127-0.1800.0240.0010.2070.0680.0590.1270.2410.2350.1440.291
height-0.3851.0000.6800.3420.2730.2750.0560.1170.045-0.020-0.190-0.0090.1500.5850.0090.4620.0830.2470.3230.7440.0990.1000.3610.375
weight-0.1730.6801.0000.7810.1710.1720.2640.2770.2020.065-0.3480.0790.3520.5460.0250.4190.2130.4410.4620.5880.0500.0510.2820.260
waistline0.1650.3420.7811.000-0.012-0.0090.3500.3030.2860.079-0.3810.0870.4150.3940.0420.3120.2780.4530.4710.0430.0030.0030.0230.016
sight_left-0.3870.2730.171-0.0121.0000.717-0.091-0.013-0.0850.0200.0090.017-0.0080.181-0.0220.087-0.0410.0400.0440.1430.0780.0750.0700.132
sight_right-0.3800.2750.172-0.0090.7171.000-0.088-0.010-0.0840.0210.0050.018-0.0060.183-0.0230.090-0.0380.0420.0460.1470.0760.0760.0710.132
SBP0.2620.0560.2640.350-0.091-0.0881.0000.7250.2430.071-0.1410.0390.2510.1840.0400.1320.2030.2320.2780.2030.0530.0550.0910.050
DBP0.1190.1170.2770.303-0.013-0.0100.7251.0000.1920.112-0.1170.0720.2470.2490.0310.1370.1830.2320.2880.1940.0070.0080.0980.093
BLDS0.2540.0450.2020.286-0.085-0.0840.2430.1921.0000.046-0.1510.0080.2630.1320.0540.1270.1480.2280.2790.1150.0460.0470.0750.014
tot_chole0.024-0.0200.0650.0790.0200.0210.0710.1120.0461.0000.1580.8870.2750.115-0.0080.0250.1030.1260.1560.0160.0030.0050.0060.008
HDL_chole-0.114-0.190-0.348-0.3810.0090.005-0.141-0.117-0.1510.1581.000-0.042-0.469-0.241-0.024-0.227-0.104-0.249-0.2220.0010.0000.0000.0000.000
LDL_chole0.039-0.0090.0790.0870.0170.0180.0390.0720.0080.887-0.0421.0000.1090.106-0.0140.0440.0550.0900.0740.0000.0000.0030.0020.000
triglyceride0.1270.1500.3520.415-0.008-0.0060.2510.2470.2630.275-0.4690.1091.0000.2960.0300.1890.2230.3610.4490.0290.0010.0000.0240.025
hemoglobin-0.1800.5850.5460.3940.1810.1830.1840.2490.1320.115-0.2410.1060.2961.0000.0170.4580.2430.4180.4680.6210.0350.0360.3110.280
urine_protein0.0240.0090.0250.042-0.022-0.0230.0400.0310.054-0.008-0.024-0.0140.0300.0171.0000.0370.0250.0210.0380.0200.0210.0190.0140.017
serum_creatinine0.0010.4620.4190.3120.0870.0900.1320.1370.1270.025-0.2270.0440.1890.4580.0371.0000.1820.2460.3210.0080.0020.0000.0030.006
SGOT_AST0.2070.0830.2130.278-0.041-0.0380.2030.1830.1480.103-0.1040.0550.2230.2430.0250.1821.0000.7310.4630.0010.0000.0000.0010.001
SGOT_ALT0.0680.2470.4410.4530.0400.0420.2320.2320.2280.126-0.2490.0900.3610.4180.0210.2460.7311.0000.6190.0020.0000.0000.0020.000
gamma_GTP0.0590.3230.4620.4710.0440.0460.2780.2880.2790.156-0.2220.0740.4490.4680.0380.3210.4630.6191.0000.1640.0060.0070.1210.153
sex0.1270.7440.5880.0430.1430.1470.2030.1940.1150.0160.0010.0000.0290.6210.0200.0080.0010.0020.1641.0000.0030.0000.6430.369
hear_left0.2410.0990.0500.0030.0780.0760.0530.0070.0460.0030.0000.0000.0010.0350.0210.0020.0000.0000.0060.0031.0000.5370.0320.058
hear_right0.2350.1000.0510.0030.0750.0760.0550.0080.0470.0050.0000.0030.0000.0360.0190.0000.0000.0000.0070.0000.5371.0000.0310.058
SMK_stat_type_cd0.1440.3610.2820.0230.0700.0710.0910.0980.0750.0060.0000.0020.0240.3110.0140.0030.0010.0020.1210.6430.0320.0311.0000.365
DRK_YN0.2910.3750.2600.0160.1320.1320.0500.0930.0140.0080.0000.0000.0250.2800.0170.0060.0010.0000.1530.3690.0580.0580.3651.000

Missing values

2023-10-15T14:46:02.022491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-15T14:46:04.856092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

sexageheightweightwaistlinesight_leftsight_righthear_lefthear_rightSBPDBPBLDStot_choleHDL_choleLDL_choletriglyceridehemoglobinurine_proteinserum_creatinineSGOT_ASTSGOT_ALTgamma_GTPSMK_stat_type_cdDRK_YN
0Male351707590.01.01.01.01.0120.080.099.0193.048.0126.092.017.11.01.021.035.040.01.0Y
1Male301808089.00.91.21.01.0130.082.0106.0228.055.0148.0121.015.81.00.920.036.027.03.0N
2Male401657591.01.21.51.01.0120.070.098.0136.041.074.0104.015.81.00.947.032.068.01.0N
3Male501758091.01.51.21.01.0145.087.095.0201.076.0104.0106.017.61.01.129.034.018.01.0N
4Male501656080.01.01.21.01.0138.082.0101.0199.061.0117.0104.013.81.00.819.012.025.01.0N
5Male501655575.01.21.51.01.0142.092.099.0218.077.095.0232.013.83.00.829.040.037.03.0Y
6Female451505569.00.50.41.01.0101.058.089.0196.066.0115.075.012.31.00.819.012.012.01.0N
7Male351756584.21.21.01.01.0132.080.094.0185.058.0107.0101.014.41.00.818.018.035.03.0Y
8Male551707584.01.20.91.01.0145.085.0104.0217.056.0141.0100.015.11.00.832.023.026.01.0Y
9Male401757582.01.51.51.01.0132.0105.0100.0195.060.0118.083.013.91.00.921.038.016.02.0Y
sexageheightweightwaistlinesight_leftsight_righthear_lefthear_rightSBPDBPBLDStot_choleHDL_choleLDL_choletriglyceridehemoglobinurine_proteinserum_creatinineSGOT_ASTSGOT_ALTgamma_GTPSMK_stat_type_cdDRK_YN
991336Male801706074.01.00.91.01.0139.083.0109.0171.075.084.057.012.01.01.218.011.015.02.0Y
991337Female351657081.01.01.01.01.0113.069.081.0173.063.092.088.013.31.00.720.017.012.01.0N
991338Male201756574.51.01.51.01.0105.070.087.0211.072.0120.092.015.41.00.825.026.050.02.0Y
991339Male701656078.00.90.81.01.0137.078.093.0167.057.089.0105.016.11.01.023.013.032.01.0Y
991340Female501505072.61.01.01.01.0116.074.0108.0178.048.0105.0125.015.21.00.828.026.029.01.0N
991341Male451758092.11.51.51.01.0114.080.088.0198.046.0125.0132.015.01.01.026.036.027.01.0N
991342Male351707586.01.01.51.01.0119.083.083.0133.040.084.045.015.81.01.114.017.015.01.0N
991343Female401555068.01.00.71.01.0110.070.090.0205.096.077.0157.014.31.00.830.027.017.03.0Y
991344Male251756072.01.51.01.01.0119.074.069.0122.038.073.053.014.51.00.821.014.017.01.0N
991345Male501607090.51.01.51.01.0133.079.099.0225.039.0153.0163.015.81.00.924.043.036.03.0Y

Duplicate rows

Most frequently occurring

sexageheightweightwaistlinesight_leftsight_righthear_lefthear_rightSBPDBPBLDStot_choleHDL_choleLDL_choletriglyceridehemoglobinurine_proteinserum_creatinineSGOT_ASTSGOT_ALTgamma_GTPSMK_stat_type_cdDRK_YN# duplicates
0Female201605070.01.01.01.01.0106.068.076.0154.045.098.056.012.71.00.818.013.011.01.0N2
1Female301504560.01.20.91.01.0100.060.0106.0181.072.090.093.012.01.00.916.011.024.01.0N2
2Female401605567.02.01.51.01.0120.080.097.0184.064.0102.087.013.01.00.716.011.043.02.0Y2
3Female401708588.00.90.91.01.0120.070.0110.0191.047.0121.0115.010.41.00.917.014.033.01.0N2
4Female451656582.01.01.01.01.0120.080.087.0178.064.0103.053.013.61.00.517.019.028.01.0N2
5Female501557090.81.01.01.01.0150.096.0101.0230.043.0150.0183.014.91.00.824.022.042.01.0N2
6Female551405078.00.91.21.01.0134.088.081.0208.044.0121.0456.013.81.00.521.024.027.01.0N2
7Female651455076.01.00.91.01.0154.096.086.0268.065.0160.0212.016.21.00.721.022.025.01.0N2
8Female651505586.00.90.91.01.0120.065.099.0228.062.0139.0136.011.91.00.727.018.014.01.0N2
9Female651555569.20.70.71.01.0130.080.0125.0294.052.0185.0283.013.83.00.633.030.024.01.0N2